from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 1s

sklearnex_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

sklearnex_LogisticRegression: 0h 0m 4s

sklearnex_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 21s

KMeans_tall: 0h 0m 25s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 29s

KNeighborsClassifier_kd_tree: 0h 2m 46s

sklearnex_KNeighborsClassifier: 0h 2m 50s

catboost_lossguide: 0h 5m 8s

xgboost: 0h 5m 9s

catboost_symmetric: 0h 5m 19s

lightgbm: 0h 5m 28s

HistGradientBoostingClassifier: 0h 5m 55s

KNeighborsClassifier: 0h 33m 38s

total: 1h 8m 4s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.146	0.0	5.497	0.0	1	100	NaN	NaN	0.497	0.0	0.293	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.0	6.136	0.0	-1	1	NaN	NaN	0.473	0.0	0.275	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.133	0.0	6.022	0.0	-1	5	NaN	NaN	0.473	0.0	0.281	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.134	0.0	5.983	0.0	1	1	NaN	NaN	0.473	0.0	0.283	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.0	6.458	0.0	-1	100	NaN	NaN	0.473	0.0	0.262	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.123	0.0	6.490	0.0	1	5	NaN	NaN	0.473	0.0	0.260	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.0	0.264	0.0	1	100	NaN	NaN	0.112	0.0	0.541	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.0	0.265	0.0	-1	1	NaN	NaN	0.112	0.0	0.537	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.0	0.262	0.0	-1	5	NaN	NaN	0.112	0.0	0.547	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.0	0.264	0.0	1	1	NaN	NaN	0.112	0.0	0.540	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.0	0.267	0.0	-1	100	NaN	NaN	0.111	0.0	0.537	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.0	0.261	0.0	1	5	NaN	NaN	0.112	0.0	0.548	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.701	0.298	0.0	0.024	1	100	0.939	0.697	2.012	0.018	11.777	0.183	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.196	0.003	0.0	0.196	1	100	1.000	0.000	0.081	0.001	2.416	0.037	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.265	0.240	0.0	0.024	-1	1	0.710	0.784	2.012	0.016	12.061	0.154	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.018	0.0	0.183	-1	1	0.000	1.000	0.082	0.003	2.229	0.236	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.318	0.000	0.0	0.035	-1	5	0.810	0.784	2.007	0.011	17.595	0.092	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.188	0.016	0.0	0.188	-1	5	0.000	1.000	0.081	0.003	2.323	0.216	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.898	0.021	0.0	0.013	1	1	0.710	0.929	2.077	0.014	6.211	0.043	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.186	0.000	0.0	0.186	1	1	0.000	1.000	0.083	0.002	2.243	0.045	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	34.925	0.000	0.0	0.035	-1	100	0.939	0.697	2.010	0.016	17.377	0.135	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.176	0.017	0.0	0.176	-1	100	1.000	0.000	0.081	0.001	2.178	0.215	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.725	0.061	0.0	0.024	1	5	0.810	0.929	2.071	0.013	11.458	0.078	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.193	0.002	0.0	0.193	1	5	0.000	1.000	0.088	0.013	2.206	0.325	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.843	0.013	0.0	0.020	1	100	0.989	0.981	0.318	0.006	62.455	1.205	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.000	0.0	0.031	1	100	1.000	1.000	0.006	0.001	4.891	0.829	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.366	0.163	0.0	0.021	-1	1	0.979	0.985	0.322	0.005	66.387	1.103	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.002	0.0	0.022	-1	1	1.000	1.000	0.006	0.000	3.493	0.388	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.280	0.000	0.0	0.031	-1	5	0.988	0.985	0.319	0.005	98.141	1.585	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.038	0.003	0.0	0.038	-1	5	1.000	1.000	0.006	0.000	6.168	0.491	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.002	0.017	0.0	0.010	1	1	0.979	0.989	0.375	0.005	26.690	0.341	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.0	0.015	1	1	1.000	1.000	0.006	0.000	2.413	0.158	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.785	0.000	0.0	0.031	-1	100	0.989	0.981	0.313	0.005	98.312	1.461	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.038	0.003	0.0	0.038	-1	100	1.000	1.000	0.006	0.001	6.094	0.990	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.834	0.010	0.0	0.020	1	5	0.988	0.989	0.376	0.006	52.768	0.822	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.000	0.0	0.031	1	5	1.000	1.000	0.007	0.001	4.825	0.663	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.105	0.0	0.026	0.0	-1	1	NaN	NaN	0.765	0.0	4.057	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.202	0.0	0.025	0.0	1	100	NaN	NaN	0.751	0.0	4.263	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.158	0.0	0.025	0.0	1	5	NaN	NaN	0.778	0.0	4.058	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.173	0.0	0.025	0.0	1	1	NaN	NaN	0.787	0.0	4.032	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.174	0.0	0.025	0.0	-1	100	NaN	NaN	0.773	0.0	4.104	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.121	0.0	0.026	0.0	-1	5	NaN	NaN	0.787	0.0	3.968	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.837	0.0	0.019	0.0	-1	1	NaN	NaN	0.566	0.0	1.480	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.865	0.0	0.018	0.0	1	100	NaN	NaN	0.533	0.0	1.624	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.845	0.0	0.019	0.0	1	5	NaN	NaN	0.530	0.0	1.595	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.846	0.0	0.019	0.0	1	1	NaN	NaN	0.531	0.0	1.595	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.855	0.0	0.019	0.0	-1	100	NaN	NaN	0.537	0.0	1.592	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.881	0.0	0.018	0.0	-1	5	NaN	NaN	0.526	0.0	1.677	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.431	0.010	0.000	0.000	-1	1	0.967	0.980	0.186	0.003	2.316	0.068	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	-1	1	1.000	1.000	0.000	0.000	14.472	6.571	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.883	0.016	0.000	0.005	1	100	0.978	0.960	0.104	0.003	46.844	1.165	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.018	0.001	0.000	0.018	1	100	0.000	1.000	0.000	0.000	54.952	22.419	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.462	0.007	0.000	0.001	1	5	0.981	0.960	0.104	0.002	14.083	0.288	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.008	0.001	0.000	0.008	1	5	0.000	1.000	0.000	0.000	24.206	10.088	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.752	0.003	0.000	0.001	1	1	0.967	0.980	0.191	0.003	3.939	0.071	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	1	1.000	1.000	0.000	0.000	10.416	4.582	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.892	0.044	0.000	0.003	-1	100	0.978	0.979	0.563	0.014	5.139	0.151	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.022	0.001	0.000	0.022	-1	100	0.000	1.000	0.001	0.000	28.096	8.888	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.893	0.028	0.000	0.001	-1	5	0.981	0.979	0.558	0.004	1.602	0.051	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.012	0.001	0.000	0.012	-1	5	0.000	1.000	0.001	0.000	15.209	5.147	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.028	0.001	0.001	0.000	-1	1	0.971	0.989	0.001	0.001	21.602	8.600	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	21.059	15.768	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.053	0.001	0.000	0.000	1	100	0.980	0.980	0.001	0.000	60.675	25.118	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.367	3.389	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.001	0.001	0.000	1	5	0.981	0.980	0.002	0.002	15.118	19.032	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.663	4.010	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.000	0.001	0.000	1	1	0.971	0.989	0.001	0.000	21.264	4.942	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.470	3.800	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.051	0.002	0.000	0.000	-1	100	0.980	0.989	0.008	0.001	6.486	0.904	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	23.348	16.811	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.001	0.001	0.000	-1	5	0.981	0.989	0.008	0.001	3.765	0.366	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	19.819	14.067	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.702	0.0	0.684	0.0	random	NaN	30	NaN	0.491	0.0	1.429	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.670	0.0	0.717	0.0	k-means++	NaN	30	NaN	0.507	0.0	1.321	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.764	0.0	3.548	0.0	random	NaN	30	NaN	2.719	0.0	2.488	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.838	0.0	3.510	0.0	k-means++	NaN	30	NaN	2.807	0.0	2.436	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.299	0.000	random	0.000	30	0.001	0.0	0.0	10.270	6.254	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.0	0.0	11.432	7.104	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.301	0.000	k-means++	0.001	30	0.001	0.0	0.0	9.439	5.100	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	11.421	7.365	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	12.503	0.000	random	0.001	30	0.002	0.0	0.0	5.876	2.080	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.013	0.002	random	1.000	30	1.000	0.0	0.0	11.621	7.887	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	11.718	0.000	k-means++	0.003	30	0.001	0.0	0.0	6.726	2.478	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.015	0.002	k-means++	1.000	30	1.000	0.0	0.0	11.255	6.868	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	20	0.095	0.0	0.034	0.0	random	NaN	20	NaN	0.035	0.0	2.712	0.0	See	See
3	KMeans_short	fit	10000	10000	2	20	0.254	0.0	0.013	0.0	k-means++	NaN	20	NaN	0.106	0.0	2.391	0.0	See	See
6	KMeans_short	fit	10000	10000	100	20	0.230	0.0	0.696	0.0	random	NaN	20	NaN	0.134	0.0	1.719	0.0	See	See
9	KMeans_short	fit	10000	10000	100	20	0.715	0.0	0.224	0.0	k-means++	NaN	20	NaN	0.361	0.0	1.980	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.155	0.000	random	0.004	20	0.000	0.001	0.0	3.500	0.746	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	random	1.000	20	1.000	0.000	0.0	10.573	5.973	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.154	0.000	k-means++	0.003	20	-0.001	0.001	0.0	3.129	0.748	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	11.604	7.235	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.517	0.000	random	0.268	20	0.311	0.001	0.0	2.343	0.569	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	9.007	4.537	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.001	4.907	0.000	k-means++	0.308	20	0.316	0.001	0.0	2.799	0.779	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.951	5.023	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	12.245	0.0	[-0.09635366]	0.000	NaN	NaN	NaN	NaN	NaN	1.936	0.0	6.325	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.793	0.0	[2.62206356]	0.001	NaN	NaN	NaN	NaN	NaN	0.752	0.0	1.054	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[48.04292635]	0.0	NaN	NaN	NaN	NaN	0.521	0.000	0.0	0.827	0.315	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.210261]	0.0	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.413	0.350	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[118.92281543]	0.0	NaN	NaN	NaN	NaN	0.280	0.003	0.0	0.545	0.090	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[17.99015386]	0.0	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.150	0.088	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.188	0.0	0.425	0.0	NaN	NaN	NaN	0.194	0.0	0.970	0.0	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.538	0.0	0.520	0.0	NaN	NaN	NaN	0.255	0.0	6.026	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	NaN	0.009	0.0	8.518	0.0	NaN	NaN	0.066	0.015	0.001	0.625	0.034	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	0.918	0.0	NaN	NaN	NaN	0.000	0.000	0.700	0.579	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	4.699	0.0	NaN	NaN	1.000	0.000	0.000	0.689	0.433	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.000	0.646	0.651	See	See